Data Analysis and Visualization¶

In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
In [2]:
df = pd.read_csv('DM_Package/Environment_Temperature_change_E_All_Data_NOFLAG.csv', encoding='cp1252')
In [3]:
df.head()
Out[3]:
Area Code Area Months Code Months Element Code Element Unit Y1961 Y1962 Y1963 ... Y2010 Y2011 Y2012 Y2013 Y2014 Y2015 Y2016 Y2017 Y2018 Y2019
0 2 Afghanistan 7001 January 7271 Temperature change °C 0.777 0.062 2.744 ... 3.601 1.179 -0.583 1.233 1.755 1.943 3.416 1.201 1.996 2.951
1 2 Afghanistan 7001 January 6078 Standard Deviation °C 1.950 1.950 1.950 ... 1.950 1.950 1.950 1.950 1.950 1.950 1.950 1.950 1.950 1.950
2 2 Afghanistan 7002 February 7271 Temperature change °C -1.743 2.465 3.919 ... 1.212 0.321 -3.201 1.494 -3.187 2.699 2.251 -0.323 2.705 0.086
3 2 Afghanistan 7002 February 6078 Standard Deviation °C 2.597 2.597 2.597 ... 2.597 2.597 2.597 2.597 2.597 2.597 2.597 2.597 2.597 2.597
4 2 Afghanistan 7003 March 7271 Temperature change °C 0.516 1.336 0.403 ... 3.390 0.748 -0.527 2.246 -0.076 -0.497 2.296 0.834 4.418 0.234

5 rows × 66 columns

In [4]:
df.isnull().sum()
Out[4]:
Area Code          0
Area               0
Months Code        0
Months             0
Element Code       0
                ... 
Y2015           1295
Y2016           1308
Y2017           1290
Y2018           1307
Y2019           1291
Length: 66, dtype: int64
In [5]:
df = df.drop(['Area Code', 'Months Code', 'Element Code', 'Unit'], axis = 1)
In [6]:
df.head()
Out[6]:
Area Months Element Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 Y1967 ... Y2010 Y2011 Y2012 Y2013 Y2014 Y2015 Y2016 Y2017 Y2018 Y2019
0 Afghanistan January Temperature change 0.777 0.062 2.744 -5.232 1.868 3.629 -1.432 ... 3.601 1.179 -0.583 1.233 1.755 1.943 3.416 1.201 1.996 2.951
1 Afghanistan January Standard Deviation 1.950 1.950 1.950 1.950 1.950 1.950 1.950 ... 1.950 1.950 1.950 1.950 1.950 1.950 1.950 1.950 1.950 1.950
2 Afghanistan February Temperature change -1.743 2.465 3.919 -0.202 -0.096 3.397 0.296 ... 1.212 0.321 -3.201 1.494 -3.187 2.699 2.251 -0.323 2.705 0.086
3 Afghanistan February Standard Deviation 2.597 2.597 2.597 2.597 2.597 2.597 2.597 ... 2.597 2.597 2.597 2.597 2.597 2.597 2.597 2.597 2.597 2.597
4 Afghanistan March Temperature change 0.516 1.336 0.403 1.659 -0.909 -0.069 -0.759 ... 3.390 0.748 -0.527 2.246 -0.076 -0.497 2.296 0.834 4.418 0.234

5 rows × 62 columns

In [7]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9656 entries, 0 to 9655
Data columns (total 62 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   Area     9656 non-null   object 
 1   Months   9656 non-null   object 
 2   Element  9656 non-null   object 
 3   Y1961    8287 non-null   float64
 4   Y1962    8322 non-null   float64
 5   Y1963    8294 non-null   float64
 6   Y1964    8252 non-null   float64
 7   Y1965    8281 non-null   float64
 8   Y1966    8364 non-null   float64
 9   Y1967    8347 non-null   float64
 10  Y1968    8345 non-null   float64
 11  Y1969    8326 non-null   float64
 12  Y1970    8308 non-null   float64
 13  Y1971    8303 non-null   float64
 14  Y1972    8323 non-null   float64
 15  Y1973    8394 non-null   float64
 16  Y1974    8374 non-null   float64
 17  Y1975    8280 non-null   float64
 18  Y1976    8209 non-null   float64
 19  Y1977    8257 non-null   float64
 20  Y1978    8327 non-null   float64
 21  Y1979    8290 non-null   float64
 22  Y1980    8283 non-null   float64
 23  Y1981    8276 non-null   float64
 24  Y1982    8237 non-null   float64
 25  Y1983    8205 non-null   float64
 26  Y1984    8259 non-null   float64
 27  Y1985    8216 non-null   float64
 28  Y1986    8268 non-null   float64
 29  Y1987    8284 non-null   float64
 30  Y1988    8273 non-null   float64
 31  Y1989    8257 non-null   float64
 32  Y1990    8239 non-null   float64
 33  Y1991    8158 non-null   float64
 34  Y1992    8354 non-null   float64
 35  Y1993    8315 non-null   float64
 36  Y1994    8373 non-null   float64
 37  Y1995    8409 non-null   float64
 38  Y1996    8439 non-null   float64
 39  Y1997    8309 non-null   float64
 40  Y1998    8370 non-null   float64
 41  Y1999    8324 non-null   float64
 42  Y2000    8342 non-null   float64
 43  Y2001    8241 non-null   float64
 44  Y2002    8312 non-null   float64
 45  Y2003    8390 non-null   float64
 46  Y2004    8415 non-null   float64
 47  Y2005    8424 non-null   float64
 48  Y2006    8503 non-null   float64
 49  Y2007    8534 non-null   float64
 50  Y2008    8475 non-null   float64
 51  Y2009    8419 non-null   float64
 52  Y2010    8435 non-null   float64
 53  Y2011    8437 non-null   float64
 54  Y2012    8350 non-null   float64
 55  Y2013    8427 non-null   float64
 56  Y2014    8377 non-null   float64
 57  Y2015    8361 non-null   float64
 58  Y2016    8348 non-null   float64
 59  Y2017    8366 non-null   float64
 60  Y2018    8349 non-null   float64
 61  Y2019    8365 non-null   float64
dtypes: float64(59), object(3)
memory usage: 4.6+ MB
In [8]:
df.shape
Out[8]:
(9656, 62)
In [9]:
df_tempchange = df[df['Element'] == 'Temperature change']
In [10]:
df_tempchange.head()
Out[10]:
Area Months Element Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 Y1967 ... Y2010 Y2011 Y2012 Y2013 Y2014 Y2015 Y2016 Y2017 Y2018 Y2019
0 Afghanistan January Temperature change 0.777 0.062 2.744 -5.232 1.868 3.629 -1.432 ... 3.601 1.179 -0.583 1.233 1.755 1.943 3.416 1.201 1.996 2.951
2 Afghanistan February Temperature change -1.743 2.465 3.919 -0.202 -0.096 3.397 0.296 ... 1.212 0.321 -3.201 1.494 -3.187 2.699 2.251 -0.323 2.705 0.086
4 Afghanistan March Temperature change 0.516 1.336 0.403 1.659 -0.909 -0.069 -0.759 ... 3.390 0.748 -0.527 2.246 -0.076 -0.497 2.296 0.834 4.418 0.234
6 Afghanistan April Temperature change -1.709 0.117 0.919 -0.533 -1.816 -1.192 -1.496 ... 2.591 1.712 1.417 -0.052 0.585 1.589 0.980 1.252 1.442 0.899
8 Afghanistan May Temperature change 1.412 -0.092 -0.690 -0.160 -0.190 -0.192 -1.307 ... 1.419 3.643 0.909 1.201 0.959 1.862 3.246 3.280 0.855 0.647

5 rows × 62 columns

In [11]:
df_tempchange.info()

<class 'pandas.core.frame.DataFrame'>
Index: 4828 entries, 0 to 9654
Data columns (total 62 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   Area     4828 non-null   object 
 1   Months   4828 non-null   object 
 2   Element  4828 non-null   object 
 3   Y1961    4152 non-null   float64
 4   Y1962    4161 non-null   float64
 5   Y1963    4147 non-null   float64
 6   Y1964    4127 non-null   float64
 7   Y1965    4149 non-null   float64
 8   Y1966    4199 non-null   float64
 9   Y1967    4182 non-null   float64
 10  Y1968    4186 non-null   float64
 11  Y1969    4178 non-null   float64
 12  Y1970    4171 non-null   float64
 13  Y1971    4172 non-null   float64
 14  Y1972    4181 non-null   float64
 15  Y1973    4229 non-null   float64
 16  Y1974    4221 non-null   float64
 17  Y1975    4174 non-null   float64
 18  Y1976    4138 non-null   float64
 19  Y1977    4171 non-null   float64
 20  Y1978    4206 non-null   float64
 21  Y1979    4186 non-null   float64
 22  Y1980    4183 non-null   float64
 23  Y1981    4175 non-null   float64
 24  Y1982    4153 non-null   float64
 25  Y1983    4135 non-null   float64
 26  Y1984    4168 non-null   float64
 27  Y1985    4143 non-null   float64
 28  Y1986    4167 non-null   float64
 29  Y1987    4176 non-null   float64
 30  Y1988    4175 non-null   float64
 31  Y1989    4171 non-null   float64
 32  Y1990    4162 non-null   float64
 33  Y1991    4139 non-null   float64
 34  Y1992    4423 non-null   float64
 35  Y1993    4425 non-null   float64
 36  Y1994    4450 non-null   float64
 37  Y1995    4468 non-null   float64
 38  Y1996    4491 non-null   float64
 39  Y1997    4419 non-null   float64
 40  Y1998    4457 non-null   float64
 41  Y1999    4434 non-null   float64
 42  Y2000    4458 non-null   float64
 43  Y2001    4400 non-null   float64
 44  Y2002    4436 non-null   float64
 45  Y2003    4483 non-null   float64
 46  Y2004    4496 non-null   float64
 47  Y2005    4501 non-null   float64
 48  Y2006    4547 non-null   float64
 49  Y2007    4563 non-null   float64
 50  Y2008    4530 non-null   float64
 51  Y2009    4507 non-null   float64
 52  Y2010    4510 non-null   float64
 53  Y2011    4529 non-null   float64
 54  Y2012    4483 non-null   float64
 55  Y2013    4527 non-null   float64
 56  Y2014    4501 non-null   float64
 57  Y2015    4494 non-null   float64
 58  Y2016    4484 non-null   float64
 59  Y2017    4496 non-null   float64
 60  Y2018    4486 non-null   float64
 61  Y2019    4486 non-null   float64
dtypes: float64(59), object(3)
memory usage: 2.3+ MB
In [12]:
df_tempchange = df_tempchange[df_tempchange['Months'].isin(['January', 'February', 'March', 'April', 'May', 'June', 'July',
       'August', 'September', 'October', 'November', 'December'])]
df_tempchange.head()
Out[12]:
Area Months Element Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 Y1967 ... Y2010 Y2011 Y2012 Y2013 Y2014 Y2015 Y2016 Y2017 Y2018 Y2019
0 Afghanistan January Temperature change 0.777 0.062 2.744 -5.232 1.868 3.629 -1.432 ... 3.601 1.179 -0.583 1.233 1.755 1.943 3.416 1.201 1.996 2.951
2 Afghanistan February Temperature change -1.743 2.465 3.919 -0.202 -0.096 3.397 0.296 ... 1.212 0.321 -3.201 1.494 -3.187 2.699 2.251 -0.323 2.705 0.086
4 Afghanistan March Temperature change 0.516 1.336 0.403 1.659 -0.909 -0.069 -0.759 ... 3.390 0.748 -0.527 2.246 -0.076 -0.497 2.296 0.834 4.418 0.234
6 Afghanistan April Temperature change -1.709 0.117 0.919 -0.533 -1.816 -1.192 -1.496 ... 2.591 1.712 1.417 -0.052 0.585 1.589 0.980 1.252 1.442 0.899
8 Afghanistan May Temperature change 1.412 -0.092 -0.690 -0.160 -0.190 -0.192 -1.307 ... 1.419 3.643 0.909 1.201 0.959 1.862 3.246 3.280 0.855 0.647

5 rows × 62 columns

In [13]:
df_tempchange.columns = df_tempchange.columns.str.replace('Y', '')
df_tempchange.head()
Out[13]:
Area Months Element 1961 1962 1963 1964 1965 1966 1967 ... 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
0 Afghanistan January Temperature change 0.777 0.062 2.744 -5.232 1.868 3.629 -1.432 ... 3.601 1.179 -0.583 1.233 1.755 1.943 3.416 1.201 1.996 2.951
2 Afghanistan February Temperature change -1.743 2.465 3.919 -0.202 -0.096 3.397 0.296 ... 1.212 0.321 -3.201 1.494 -3.187 2.699 2.251 -0.323 2.705 0.086
4 Afghanistan March Temperature change 0.516 1.336 0.403 1.659 -0.909 -0.069 -0.759 ... 3.390 0.748 -0.527 2.246 -0.076 -0.497 2.296 0.834 4.418 0.234
6 Afghanistan April Temperature change -1.709 0.117 0.919 -0.533 -1.816 -1.192 -1.496 ... 2.591 1.712 1.417 -0.052 0.585 1.589 0.980 1.252 1.442 0.899
8 Afghanistan May Temperature change 1.412 -0.092 -0.690 -0.160 -0.190 -0.192 -1.307 ... 1.419 3.643 0.909 1.201 0.959 1.862 3.246 3.280 0.855 0.647

5 rows × 62 columns

In [103]:
df_final = pd.melt(df_tempchange, id_vars = ['Area', 'Months', 'Element'], value_vars = ['1961','1962','1963','1964','1965','1966','1967','1968','1969','1970','1971','1972','1973','1974','1975','1976','1977','1978','1979','1980','1981','1982','1983','1984','1985','1986','1987','1988','1989','1990','1991','1992','1993','1994','1995','1996','1997','1998','1999','2000','2001','2002','2003','2004','2005','2006','2007','2008','2009','2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'], var_name = 'Year', value_name = 'Temperature')
In [104]:
df_final.head()
Out[104]:
Area Months Element Year Temperature
0 Afghanistan January Temperature change 1961 0.777
1 Afghanistan February Temperature change 1961 -1.743
2 Afghanistan March Temperature change 1961 0.516
3 Afghanistan April Temperature change 1961 -1.709
4 Afghanistan May Temperature change 1961 1.412
In [105]:
df_final['Year'] = df_final['Year'].astype(int)
In [87]:
df_final['Area'].unique()
Out[87]:
array(['Afghanistan', 'Albania', 'Algeria', 'American Samoa', 'Andorra',
       'Angola', 'Anguilla', 'Antarctica', 'Antigua and Barbuda',
       'Argentina', 'Armenia', 'Aruba', 'Australia', 'Austria',
       'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados',
       'Belarus', 'Belgium', 'Belgium-Luxembourg', 'Belize', 'Benin',
       'Bhutan', 'Bolivia (Plurinational State of)',
       'Bosnia and Herzegovina', 'Botswana', 'Brazil',
       'British Virgin Islands', 'Brunei Darussalam', 'Bulgaria',
       'Burkina Faso', 'Burundi', 'Cabo Verde', 'Cambodia', 'Cameroon',
       'Canada', 'Cayman Islands', 'Central African Republic', 'Chad',
       'Channel Islands', 'Chile', 'China', 'China, Hong Kong SAR',
       'China, Macao SAR', 'China, mainland', 'China, Taiwan Province of',
       'Christmas Island', 'Cocos (Keeling) Islands', 'Colombia',
       'Comoros', 'Congo', 'Cook Islands', 'Costa Rica', "Côte d'Ivoire",
       'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Czechoslovakia',
       "Democratic People's Republic of Korea",
       'Democratic Republic of the Congo', 'Denmark', 'Djibouti',
       'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',
       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',
       'Eswatini', 'Ethiopia', 'Ethiopia PDR',
       'Falkland Islands (Malvinas)', 'Faroe Islands', 'Fiji', 'Finland',
       'France', 'French Guiana', 'French Polynesia',
       'French Southern and Antarctic Territories', 'Gabon', 'Gambia',
       'Georgia', 'Germany', 'Ghana', 'Gibraltar', 'Greece', 'Greenland',
       'Grenada', 'Guadeloupe', 'Guatemala', 'Guinea', 'Guinea-Bissau',
       'Guyana', 'Haiti', 'Holy See', 'Honduras', 'Hungary', 'Iceland',
       'India', 'Indonesia', 'Iran (Islamic Republic of)', 'Iraq',
       'Ireland', 'Isle of Man', 'Israel', 'Italy', 'Jamaica', 'Japan',
       'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait',
       'Kyrgyzstan', "Lao People's Democratic Republic", 'Latvia',
       'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein',
       'Lithuania', 'Luxembourg', 'Madagascar', 'Malawi', 'Malaysia',
       'Maldives', 'Mali', 'Malta', 'Marshall Islands', 'Martinique',
       'Mauritania', 'Mauritius', 'Mayotte', 'Mexico',
       'Micronesia (Federated States of)', 'Midway Island', 'Monaco',
       'Mongolia', 'Montenegro', 'Montserrat', 'Morocco', 'Mozambique',
       'Myanmar', 'Namibia', 'Nauru', 'Nepal', 'Netherlands',
       'Netherlands Antilles (former)', 'New Caledonia', 'New Zealand',
       'Nicaragua', 'Niger', 'Nigeria', 'Niue', 'Norfolk Island',
       'North Macedonia', 'Norway', 'Oman',
       'Pacific Islands Trust Territory', 'Pakistan', 'Palau',
       'Palestine', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',
       'Philippines', 'Pitcairn Islands', 'Poland', 'Portugal',
       'Puerto Rico', 'Qatar', 'Republic of Korea', 'Republic of Moldova',
       'Réunion', 'Romania', 'Russian Federation', 'Rwanda',
       'Saint Helena, Ascension and Tristan da Cunha',
       'Saint Kitts and Nevis', 'Saint Lucia',
       'Saint Pierre and Miquelon', 'Saint Vincent and the Grenadines',
       'Samoa', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia',
       'Senegal', 'Serbia', 'Serbia and Montenegro', 'Seychelles',
       'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',
       'Solomon Islands', 'Somalia', 'South Africa',
       'South Georgia and the South Sandwich Islands', 'South Sudan',
       'Spain', 'Sri Lanka', 'Sudan', 'Sudan (former)', 'Suriname',
       'Svalbard and Jan Mayen Islands', 'Sweden', 'Switzerland',
       'Syrian Arab Republic', 'Tajikistan', 'Thailand', 'Timor-Leste',
       'Togo', 'Tokelau', 'Tonga', 'Trinidad and Tobago', 'Tunisia',
       'Turkey', 'Turkmenistan', 'Turks and Caicos Islands', 'Tuvalu',
       'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom',
       'United Republic of Tanzania', 'United States of America',
       'United States Virgin Islands', 'Uruguay', 'USSR', 'Uzbekistan',
       'Vanuatu', 'Venezuela (Bolivarian Republic of)', 'Viet Nam',
       'Wake Island', 'Wallis and Futuna Islands', 'Western Sahara',
       'Yemen', 'Yugoslav SFR', 'Zambia', 'Zimbabwe', 'World', 'Africa',
       'Eastern Africa', 'Middle Africa', 'Northern Africa',
       'Southern Africa', 'Western Africa', 'Americas',
       'Northern America', 'Central America', 'Caribbean',
       'South America', 'Asia', 'Central Asia', 'Eastern Asia',
       'Southern Asia', 'South-Eastern Asia', 'Western Asia', 'Europe',
       'Eastern Europe', 'Northern Europe', 'Southern Europe',
       'Western Europe', 'Oceania', 'Australia and New Zealand',
       'Melanesia', 'Micronesia', 'Polynesia', 'European Union',
       'Least Developed Countries', 'Land Locked Developing Countries',
       'Small Island Developing States',
       'Low Income Food Deficit Countries',
       'Net Food Importing Developing Countries', 'Annex I countries',
       'Non-Annex I countries', 'OECD'], dtype=object)
In [18]:
df_final.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 201072 entries, 0 to 201071
Data columns (total 5 columns):
 #   Column       Non-Null Count   Dtype  
---  ------       --------------   -----  
 0   Area         201072 non-null  object 
 1   Months       201072 non-null  object 
 2   Element      201072 non-null  object 
 3   Year         201072 non-null  int32  
 4   Temperature  179792 non-null  float64
dtypes: float64(1), int32(1), object(3)
memory usage: 6.9+ MB
In [19]:
world = df_final.loc[df_final.Area == 'World']
world.info()

<class 'pandas.core.frame.DataFrame'>
Index: 708 entries, 2964 to 200639
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Area         708 non-null    object 
 1   Months       708 non-null    object 
 2   Element      708 non-null    object 
 3   Year         708 non-null    int32  
 4   Temperature  708 non-null    float64
dtypes: float64(1), int32(1), object(3)
memory usage: 30.4+ KB
In [20]:
mean_temperatures_by_country = df_final.groupby(['Area', 'Year'])['Temperature'].mean().reset_index()
In [21]:
def plot_mean_temperature(country):
    # Filter DataFrame for the selected country
    country_df = mean_temperatures_by_country[mean_temperatures_by_country['Area'] == country]
    if country_df.empty:
        print("Data not available for the selected country.")
        return
    
    # Plot mean temperature for the selected country using Plotly
    fig = px.line(country_df, x='Year', y='Temperature', 
                  title=f'Mean Temperature for {country}', labels={'Temperature': 'Mean Temperature Difference (°C)'})
    fig.show()
In [29]:
country_input = input("Enter the country name: ")
In [30]:
plot_mean_temperature(country_input)
In [106]:
df_final['Year'] = pd.to_numeric(df_final['Year'])
In [107]:
month_to_num = {
    'January': 1, 'February': 2, 'March': 3, 'April': 4, 'May': 5, 'June': 6,
    'July': 7, 'August': 8, 'September': 9, 'October': 10, 'November': 11, 'December': 12
}
In [108]:
df_final['Months_num'] = df_final['Months'].map(month_to_num)
In [116]:
df_final['Year'] += (df_final['Months_num'] - 1) // 12
In [119]:
df_final['Date'] = pd.to_datetime(df_final['Year'].astype(str) + '-' + df_final['Months_num'].astype(str), format='%Y-%m')
In [120]:
df_final['Decade'] = (df_final['Date'].dt.year // 10) * 10
In [121]:
df_final.head()
Out[121]:
Area Months Element Year Temperature Months_num Date Decade
0 Afghanistan January Temperature change 1961 0.777 1 1961-01-01 1960
1 Afghanistan February Temperature change 1961 -1.743 2 1961-02-01 1960
2 Afghanistan March Temperature change 1961 0.516 3 1961-03-01 1960
3 Afghanistan April Temperature change 1961 -1.709 4 1961-04-01 1960
4 Afghanistan May Temperature change 1961 1.412 5 1961-05-01 1960
In [38]:
mean_temperatures_by_decade = df_final.groupby(['Area', 'Decade'])['Temperature'].mean().reset_index()
In [39]:
def plot_mean_temperature_decade(country):
    # Filter DataFrame for the selected country
    country_df = mean_temperatures_by_decade[mean_temperatures_by_decade['Area'] == country]
    if country_df.empty:
        print("Data not available for the selected country.")
        return
    
    # Plot mean temperature for the selected country using Plotly
    fig = px.line(country_df, x='Decade', y='Temperature', 
                  title=f'Mean Temperature for {country}', labels={'Temperature': 'Mean Temperature difference (°C)'})
    fig.show()
In [40]:
country_input = input("Enter the country name: ")
In [41]:
plot_mean_temperature_decade(country_input)
In [42]:
def generate_additional_plots(country):
    # Filter the DataFrame for the specified country
    df_country = df_final[df_final['Area'] == country]

    # 1. Bar Plot of Average Temperature Change by Year
    fig1 = px.bar(df_country.groupby('Year')['Temperature'].mean().reset_index(), x='Year', y='Temperature',
                  title=f'Average Temperature Change by Year in {country}', labels={'Temperature': 'Average Temperature Change'})

    # 2. Box Plot of Temperature Change Distribution by Year
    fig2 = px.box(df_country, x='Year', y='Temperature',
                  title=f'Temperature Change Distribution by Year in {country}', labels={'Temperature': 'Temperature Change'})

    # 3. Histogram of Temperature Change
    fig3 = px.histogram(df_country, x='Temperature',
                        title=f'Temperature Change Histogram in {country}', labels={'Temperature': 'Temperature Change'})

    heatmap_data = df_country.pivot_table(index='Year', columns='Months', values='Temperature', aggfunc='mean')
    months_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
    fig4 = px.imshow(heatmap_data,
                    labels=dict(x="Month", y="Year", color="Temperature"),
                    x=months_order,
                    y=list(range(df_country['Year'].min(), df_country['Year'].max() + 1)),
                    title=f'Temperature Change by Year and Month in {country}')


    # Show the plots
    fig1.show()
    fig2.show()
    fig3.show()
    fig4.show()
In [43]:
country_name = input("Enter the name of the country: ")
In [44]:
generate_additional_plots(country_name)
In [176]:
df1 = df_final.groupby(['Area', 'Year'])['Temperature'].mean().reset_index()
df1['Year'] = pd.to_datetime(df1['Year'])
df1 = df1.sort_values('Year',ascending=True)
df1['Year'] = df1['Year'].dt.strftime('%m/%d/%Y')
In [177]:
top_10 = df1.groupby('Area').sum().sort_values('Temperature', ascending=False)[:10].reset_index()['Area']
top_10
Out[177]:
0                          Mongolia
1    Svalbard and Jan Mayen Islands
2                            Gambia
3                           Morocco
4                     Guinea-Bissau
5                           Tunisia
6                        Mauritania
7                    Eastern Europe
8                           Austria
9                           Senegal
Name: Area, dtype: object

Model Building¶

In [75]:
df_final.head()
Out[75]:
Area Months Element Year Temperature Date Decade
0 Afghanistan 1 Temperature change 1961 0.777 1961-01-01 1960
1 Afghanistan 2 Temperature change 1961 -1.743 1961-02-01 1960
2 Afghanistan 3 Temperature change 1961 0.516 1961-03-01 1960
3 Afghanistan 4 Temperature change 1961 -1.709 1961-04-01 1960
4 Afghanistan 5 Temperature change 1961 1.412 1961-05-01 1960
In [122]:
df_model = df_final.copy()
In [123]:
df_model.shape
Out[123]:
(201072, 8)
In [124]:
null_counts = df_model.isnull().sum()
fig, ax = plt.subplots()
null_counts.plot(kind='bar')
plt.xlabel('Columns')
plt.ylabel('Count')
plt.title('Number of Null Values in Each Column')
plt.show()
No description has been provided for this image
In [125]:
df_model = df_model.sort_values(by=['Area', 'Year'])
In [126]:
df_model['Temperature_Mean_Imputed'] = df_model.groupby('Area')['Temperature'].transform(lambda x: x.fillna(x.mean()))
In [127]:
df_model.isnull().sum()
Out[127]:
Area                            0
Months                          0
Element                         0
Year                            0
Temperature                 21280
Months_num                      0
Date                            0
Decade                          0
Temperature_Mean_Imputed        0
dtype: int64
In [134]:
model_df = df_model.loc[df_model.Area == 'India']
In [135]:
model_df.head()
Out[135]:
Area Months Element Year Temperature Months_num Date Decade Temperature_Mean_Imputed
1236 India January Temperature change 1961 0.536 1 1961-01-01 1960 0.536
1237 India February Temperature change 1961 -1.413 2 1961-02-01 1960 -1.413
1238 India March Temperature change 1961 0.499 3 1961-03-01 1960 0.499
1239 India April Temperature change 1961 0.026 4 1961-04-01 1960 0.026
1240 India May Temperature change 1961 -0.144 5 1961-05-01 1960 -0.144
In [137]:
model_df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 708 entries, 1236 to 198911
Data columns (total 9 columns):
 #   Column                    Non-Null Count  Dtype         
---  ------                    --------------  -----         
 0   Area                      708 non-null    object        
 1   Months                    708 non-null    object        
 2   Element                   708 non-null    object        
 3   Year                      708 non-null    int64         
 4   Temperature               708 non-null    float64       
 5   Months_num                708 non-null    int64         
 6   Date                      708 non-null    datetime64[ns]
 7   Decade                    708 non-null    int32         
 8   Temperature_Mean_Imputed  708 non-null    float64       
dtypes: datetime64[ns](1), float64(2), int32(1), int64(2), object(3)
memory usage: 52.5+ KB
In [138]:
model_df.drop(['Area', 'Temperature', 'Months_num', 'Decade', 'Element'], axis = 1, inplace = True)

C:\Users\mahit\AppData\Local\Temp\ipykernel_23028\2385096789.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [139]:
model_df.head()
Out[139]:
Months Year Date Temperature_Mean_Imputed
1236 January 1961 1961-01-01 0.536
1237 February 1961 1961-02-01 -1.413
1238 March 1961 1961-03-01 0.499
1239 April 1961 1961-04-01 0.026
1240 May 1961 1961-05-01 -0.144
In [140]:
model_df.rename(columns={'Temperature_Mean_Imputed': 'Temperature'}, inplace=True)
model_df.set_index('Date', inplace=True)

C:\Users\mahit\AppData\Local\Temp\ipykernel_23028\2662303892.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [141]:
model_df.head()
Out[141]:
Months Year Temperature
Date
1961-01-01 January 1961 0.536
1961-02-01 February 1961 -1.413
1961-03-01 March 1961 0.499
1961-04-01 April 1961 0.026
1961-05-01 May 1961 -0.144
In [160]:
rolling_mean = model_df['Temperature'].rolling(window = 12).mean()
rolling_std = model_df['Temperature'].rolling(window = 12).std()

plt.plot(model_df['Temperature'], color = 'blue', label = 'Original')
plt.plot(rolling_mean, color = 'red', label = 'Rolling Mean')
plt.plot(rolling_std, color = 'black', label = 'Rolling Std')
plt.legend(loc = 'best')
plt.title('Rolling Mean & Rolling Standard Deviation')
plt.show()
No description has been provided for this image
In [162]:
from statsmodels.tsa.seasonal import seasonal_decompose
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 15, 6
seasonal_decomposing = seasonal_decompose(model_df['Temperature'], model='additive')
seasonal_decomposing.plot()
plt.show()
No description has been provided for this image
In [164]:
from statsmodels.tsa.stattools import adfuller
def adf_test(series):
  result=adfuller(series)
  print('ADF Statistics: {}'.format(result[0]))
  print('p-value:{}'.format(result[1]))
  if result[1]<=0.05:
    print("Stationary")
  else:
    print("Not a stationary")
In [165]:
adf_test(model_df['Temperature'])

ADF Statistics: -4.561271880204758
p-value:0.00015215051585545597
Stationary
In [169]:
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
fig, ax = plt.subplots(figsize=(12,6))
ax=plot_acf(model_df['Temperature'], ax)
No description has been provided for this image
In [170]:
fig, ax
ax=plot_pacf(model_df['Temperature'], lags = 10)
No description has been provided for this image
In [ ]:
pip install statsmodels
In [ ]:
pip install tensorflow
In [145]:
from statsmodels.tsa.statespace.sarimax import SARIMAX
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
In [148]:
train = model_df[model_df.index < pd.Timestamp('2016-01-01')]
test = model_df[model_df.index >= pd.Timestamp('2016-01-01')]
In [180]:
model_df.iloc[:,2]
Out[180]:
Date
1961-01-01    0.536
1961-02-01   -1.413
1961-03-01    0.499
1961-04-01    0.026
1961-05-01   -0.144
              ...  
2019-08-01    0.751
2019-09-01    0.445
2019-10-01    0.267
2019-11-01    1.389
2019-12-01    0.181
Name: Temperature, Length: 708, dtype: float64
In [183]:
for seasonal_order in [(1,1,1,52),(1,1,1,12),(1,2,1,52),(1,1,2,52)]:

    print(seasonal_order)
    order=(1,1,1)
    
    model = SARIMAX(model_df.iloc[:,2], order=order, seasonal_order=seasonal_order)
    results = model.fit()

    #results.params

    forecast_steps = 60
    forecast = results.get_forecast(steps=forecast_steps)
    forecasted_values = forecast.predicted_mean
    confidence_intervals = forecast.conf_int()

    n=len(model_df)
    plt.plot(model_df.iloc[:,2], label='Actual')
    plt.plot(range(n,n+forecast_steps), forecasted_values, label='Forecast')
    plt.fill_between(range(n,n+forecast_steps),
                     np.array(confidence_intervals)[:,0],
                     np.array(confidence_intervals)[:,1], 
                     color='gray', alpha=0.2)
    plt.title(seasonal_order)
    plt.legend()
    plt.show()

(1, 1, 1, 52)

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency MS will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency MS will be used.
No description has been provided for this image 
(1, 1, 1, 12)

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency MS will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency MS will be used.
No description has been provided for this image 
(1, 2, 1, 52)

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency MS will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency MS will be used.
No description has been provided for this image 
(1, 1, 2, 52)

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency MS will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency MS will be used.
No description has been provided for this image
In [184]:
def train_sarima(data):
    model = SARIMAX(data, order=(1, 1, 1), seasonal_order=(1, 2, 1, 52))
    return model.fit()

sarima_models = {}
for month in df_final['Months'].unique():
    data_month = train[train['Months'] == month]['Temperature']
    sarima_models[month] = train_sarima(data_month)

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-JAN will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-JAN will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-FEB will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-FEB will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-MAR will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-MAR will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-APR will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-APR will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-MAY will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-MAY will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-JUN will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-JUN will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-JUL will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-JUL will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-AUG will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-AUG will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-SEP will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-SEP will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-OCT will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-OCT will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-NOV will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-NOV will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-DEC will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning:

No frequency information was provided, so inferred frequency YS-DEC will be used.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for ARMA and trend. All parameters except for variances will be set to zeros.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:866: UserWarning:

Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.
In [185]:
def train_lstm(data):
    scaler = MinMaxScaler()
    data_scaled = scaler.fit_transform(data.values.reshape(-1, 1))
    
    X, y = [], []
    for i in range(len(data_scaled)-12):
        X.append(data_scaled[i:i+12])
        y.append(data_scaled[i+12])
    X, y = np.array(X), np.array(y)
    
    model = Sequential()
    model.add(LSTM(50, activation='relu', input_shape=(12, 1)))
    model.add(Dense(1))
    model.compile(optimizer='adam', loss='mse')
    model.fit(X, y, epochs=100, verbose=0)
    
    return model, scaler
In [186]:
lstm_models = {}
for month in df_final['Months'].unique():
    data_month = train[train['Months'] == month]['Temperature']
    lstm_models[month] = train_lstm(data_month)

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.

C:\Users\mahit\miniconda3\envs\mreddy\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning:

Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
In [187]:
def forecast_sarima(model, steps):
    return model.forecast(steps)
In [188]:
def forecast_lstm(model, scaler, data, steps):
    data_scaled = scaler.transform(data.values.reshape(-1, 1))
    input_data = data_scaled[-12:].reshape(1, 12, 1)
    forecast_scaled = []
    for _ in range(steps):
        forecast_scaled.append(model.predict(input_data)[0, 0])
        input_data = np.append(input_data[:, 1:, :], forecast_scaled[-1].reshape(1, 1, 1), axis=1)
    forecast = scaler.inverse_transform(np.array(forecast_scaled).reshape(-1, 1))
    return forecast.flatten()
In [189]:
forecast_steps = 60
forecast_sarima_values = {}
forecast_lstm_values = {}

for month in df_final['Months'].unique():
    sarima_model = sarima_models[month]
    lstm_model, scaler = lstm_models[month]
    data_month = df_final[df_final['Months'] == month]['Temperature']
    
    forecast_sarima_values[month] = forecast_sarima(sarima_model, forecast_steps)
    forecast_lstm_values[month] = forecast_lstm(lstm_model, scaler, data_month, forecast_steps)

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In [190]:
def plot_forecast(actual, sarima_forecast, lstm_forecast, month):
    plt.figure(figsize=(10, 6))
    plt.plot(actual.index, actual.values, label='Actual', color='black')
    plt.plot(actual.index[-1] + pd.DateOffset(months=1) * np.arange(1, forecast_steps+1), sarima_forecast, label='SARIMA Forecast', linestyle='--', color='blue')
    plt.plot(actual.index[-1] + pd.DateOffset(months=1) * np.arange(1, forecast_steps+1), lstm_forecast, label='LSTM Forecast', linestyle='--', color='red')
    plt.title(f'Temperature Change Forecast for {month} in India')
    plt.xlabel('Year')
    plt.ylabel('Temperature Change')
    plt.legend()
    plt.grid(True)
    plt.show()

# Visualizing forecast for each month
for month in model_df['Months'].unique():
    actual_month = model_df[model_df['Months'] == month]['Temperature']
    plot_forecast(actual_month, forecast_sarima_values[month], forecast_lstm_values[month], month)
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In [ ]: